Zomato Exploratory Data Analysis

Step 1: Importing dataset and necessary libraries

The Dataset is from https://wwww.kaggle.com The file size is about 547MB

Step 2: Data preparation & cleaning

Load the dataset into a data frame using Pandas Explore the number of rows & columns, ranges of values etc. Handle missing, incorrect and invalid data Perform any additional steps (parsing dates, creating additional columns, merging multiple dataset etc.)

Step 3: Statistical analysis & visualization

Compute the mean, sum, range and other interesting statistics for numeric columns Explore relationship between columns using scatter plots, bar charts etc. Make a note of interesting insights from the exploratory analysis Your notebook should contain at least 8 graphs & 4 different types of graphs

Step 4: Ask & answer questions about the data

Ask at least 8 interesting questions about your dataset Answer the questions either by computing the results using Numpy/Pandas or by plotting graphs using Matplotlib/Seaborn/Plotly/Folium Create new columns, merge multiple dataset and perform grouping/aggregation wherever necessary For each question, summarize the key insight from the analysis or visualization in simple words

Step 5: Summary and conclusion

Write a summary of what you've learned from the analysis Include interesting insights and graphs from previous sections Share ideas for future work on the same topic using other relevant datasets Share links to resources you found useful during your analysis

Step 1: Importing dataset and necessary libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

Step 2: Data preparation & cleaning

Loading the csv file into the pandas dataframe

df=pd.read_csv('zomato.csv')
df

	url	address	name	online_order	book_table	rate	votes	phone	location	rest_type	dish_liked	cuisines	approx_cost(for two people)	reviews_list	menu_item	listed_in(type)	listed_in(city)
0	https://www.zomato.com/bangalore/jalsa-banasha...	942, 21st Main Road, 2nd Stage, Banashankari, ...	Jalsa	Yes	Yes	4.1/5	775	080 42297555\r\n+91 9743772233	Banashankari	Casual Dining	Pasta, Lunch Buffet, Masala Papad, Paneer Laja...	North Indian, Mughlai, Chinese	800	[('Rated 4.0', 'RATED\n A beautiful place to ...	[]	Buffet	Banashankari
1	https://www.zomato.com/bangalore/spice-elephan...	2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...	Spice Elephant	Yes	No	4.1/5	787	080 41714161	Banashankari	Casual Dining	Momos, Lunch Buffet, Chocolate Nirvana, Thai G...	Chinese, North Indian, Thai	800	[('Rated 4.0', 'RATED\n Had been here for din...	[]	Buffet	Banashankari
2	https://www.zomato.com/SanchurroBangalore?cont...	1112, Next to KIMS Medical College, 17th Cross...	San Churro Cafe	Yes	No	3.8/5	918	+91 9663487993	Banashankari	Cafe, Casual Dining	Churros, Cannelloni, Minestrone Soup, Hot Choc...	Cafe, Mexican, Italian	800	[('Rated 3.0', "RATED\n Ambience is not that ...	[]	Buffet	Banashankari
3	https://www.zomato.com/bangalore/addhuri-udupi...	1st Floor, Annakuteera, 3rd Stage, Banashankar...	Addhuri Udupi Bhojana	No	No	3.7/5	88	+91 9620009302	Banashankari	Quick Bites	Masala Dosa	South Indian, North Indian	300	[('Rated 4.0', "RATED\n Great food and proper...	[]	Buffet	Banashankari
4	https://www.zomato.com/bangalore/grand-village...	10, 3rd Floor, Lakshmi Associates, Gandhi Baza...	Grand Village	No	No	3.8/5	166	+91 8026612447\r\n+91 9901210005	Basavanagudi	Casual Dining	Panipuri, Gol Gappe	North Indian, Rajasthani	600	[('Rated 4.0', 'RATED\n Very good restaurant ...	[]	Buffet	Banashankari
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
51712	https://www.zomato.com/bangalore/best-brews-fo...	Four Points by Sheraton Bengaluru, 43/3, White...	Best Brews - Four Points by Sheraton Bengaluru...	No	No	3.6 /5	27	080 40301477	Whitefield	Bar	NaN	Continental	1,500	[('Rated 5.0', "RATED\n Food and service are ...	[]	Pubs and bars	Whitefield
51713	https://www.zomato.com/bangalore/vinod-bar-and...	Number 10, Garudachar Palya, Mahadevapura, Whi...	Vinod Bar And Restaurant	No	No	NaN	0	+91 8197675843	Whitefield	Bar	NaN	Finger Food	600	[]	[]	Pubs and bars	Whitefield
51714	https://www.zomato.com/bangalore/plunge-sherat...	Sheraton Grand Bengaluru Whitefield Hotel & Co...	Plunge - Sheraton Grand Bengaluru Whitefield H...	No	No	NaN	0	NaN	Whitefield	Bar	NaN	Finger Food	2,000	[]	[]	Pubs and bars	Whitefield
51715	https://www.zomato.com/bangalore/chime-sherato...	Sheraton Grand Bengaluru Whitefield Hotel & Co...	Chime - Sheraton Grand Bengaluru Whitefield Ho...	No	Yes	4.3 /5	236	080 49652769	ITPL Main Road, Whitefield	Bar	Cocktails, Pizza, Buttermilk	Finger Food	2,500	[('Rated 4.0', 'RATED\n Nice and friendly pla...	[]	Pubs and bars	Whitefield
51716	https://www.zomato.com/bangalore/the-nest-the-...	ITPL Main Road, KIADB Export Promotion Industr...	The Nest - The Den Bengaluru	No	No	3.4 /5	13	+91 8071117272	ITPL Main Road, Whitefield	Bar, Casual Dining	NaN	Finger Food, North Indian, Continental	1,500	[('Rated 5.0', 'RATED\n Great ambience , look...	[]	Pubs and bars	Whitefield

51717 rows × 17 columns

print('The DataFrame Contains about {} rows and columns respectively'.format(df.shape))

The DataFrame Contains about (51717, 17) rows and columns respectively

print('The DataFrame contains the folowing columns {} '.format(df.columns))

The DataFrame contains the folowing columns Index(['url', 'address', 'name', 'online_order', 'book_table', 'rate', 'votes',
       'phone', 'location', 'rest_type', 'dish_liked', 'cuisines',
       'approx_cost(for two people)', 'reviews_list', 'menu_item',
       'listed_in(type)', 'listed_in(city)'],
      dtype='object') 

print('Statistically, the following can be described for the DataFrame {} '.format(df.describe()))

Statistically, the following can be described for the DataFrame               votes
count  51717.000000
mean     283.697527
std      803.838853
min        0.000000
25%        7.000000
50%       41.000000
75%      198.000000
max    16832.000000 

print('In total, the DataFrame has the null values of columns {}'.format(df.isnull().sum()))

In total, the DataFrame has the null values of columns url                                0
address                            0
name                               0
online_order                       0
book_table                         0
rate                            7775
votes                              0
phone                           1208
location                          21
rest_type                        227
dish_liked                     28078
cuisines                          45
approx_cost(for two people)      346
reviews_list                       0
menu_item                          0
listed_in(type)                    0
listed_in(city)                    0
dtype: int64

Data cleaning

The URL column, address, Menu Column, Phone Number are not requrired. So I am going to drop those columns. Also in the other coulmuns, I will replace the null values with appropriate values. The rating is done out of 5 So I am going to keep only the rating and take out the out of 5 value. from the column.

Droping the unnecessary columns¶

df=df.drop(['url'],axis=1)
df=df.drop(['address'],axis=1)
df=df.drop(['menu_item'],axis=1)
df=df.drop(['phone'],axis=1)
df=df.drop(['reviews_list'],axis=1)
df

	name	online_order	book_table	rate	votes	location	rest_type	dish_liked	cuisines	approx_cost(for two people)	listed_in(type)	listed_in(city)
0	Jalsa	Yes	Yes	4.1/5	775	Banashankari	Casual Dining	Pasta, Lunch Buffet, Masala Papad, Paneer Laja...	North Indian, Mughlai, Chinese	800	Buffet	Banashankari
1	Spice Elephant	Yes	No	4.1/5	787	Banashankari	Casual Dining	Momos, Lunch Buffet, Chocolate Nirvana, Thai G...	Chinese, North Indian, Thai	800	Buffet	Banashankari
2	San Churro Cafe	Yes	No	3.8/5	918	Banashankari	Cafe, Casual Dining	Churros, Cannelloni, Minestrone Soup, Hot Choc...	Cafe, Mexican, Italian	800	Buffet	Banashankari
3	Addhuri Udupi Bhojana	No	No	3.7/5	88	Banashankari	Quick Bites	Masala Dosa	South Indian, North Indian	300	Buffet	Banashankari
4	Grand Village	No	No	3.8/5	166	Basavanagudi	Casual Dining	Panipuri, Gol Gappe	North Indian, Rajasthani	600	Buffet	Banashankari
...	...	...	...	...	...	...	...	...	...	...	...	...
51712	Best Brews - Four Points by Sheraton Bengaluru...	No	No	3.6 /5	27	Whitefield	Bar	NaN	Continental	1,500	Pubs and bars	Whitefield
51713	Vinod Bar And Restaurant	No	No	NaN	0	Whitefield	Bar	NaN	Finger Food	600	Pubs and bars	Whitefield
51714	Plunge - Sheraton Grand Bengaluru Whitefield H...	No	No	NaN	0	Whitefield	Bar	NaN	Finger Food	2,000	Pubs and bars	Whitefield
51715	Chime - Sheraton Grand Bengaluru Whitefield Ho...	No	Yes	4.3 /5	236	ITPL Main Road, Whitefield	Bar	Cocktails, Pizza, Buttermilk	Finger Food	2,500	Pubs and bars	Whitefield
51716	The Nest - The Den Bengaluru	No	No	3.4 /5	13	ITPL Main Road, Whitefield	Bar, Casual Dining	NaN	Finger Food, North Indian, Continental	1,500	Pubs and bars	Whitefield

51717 rows × 12 columns

=> Rate Column

df['rate'].unique()

array(['4.1/5', '3.8/5', '3.7/5', '3.6/5', '4.6/5', '4.0/5', '4.2/5',
       '3.9/5', '3.1/5', '3.0/5', '3.2/5', '3.3/5', '2.8/5', '4.4/5',
       '4.3/5', 'NEW', '2.9/5', '3.5/5', nan, '2.6/5', '3.8 /5', '3.4/5',
       '4.5/5', '2.5/5', '2.7/5', '4.7/5', '2.4/5', '2.2/5', '2.3/5',
       '3.4 /5', '-', '3.6 /5', '4.8/5', '3.9 /5', '4.2 /5', '4.0 /5',
       '4.1 /5', '3.7 /5', '3.1 /5', '2.9 /5', '3.3 /5', '2.8 /5',
       '3.5 /5', '2.7 /5', '2.5 /5', '3.2 /5', '2.6 /5', '4.5 /5',
       '4.3 /5', '4.4 /5', '4.9/5', '2.1/5', '2.0/5', '1.8/5', '4.6 /5',
       '4.9 /5', '3.0 /5', '4.8 /5', '2.3 /5', '4.7 /5', '2.4 /5',
       '2.1 /5', '2.2 /5', '2.0 /5', '1.8 /5'], dtype=object)

def convert_rate(value):
    if(value=='NEW' or value=='-'):
        return np.nan
    else:
        value=str(value).split('/')
        value=value[0]
        return float(value)
df['rate']=df['rate'].apply(convert_rate)
df.fillna(0, inplace=True)
df

	name	online_order	book_table	rate	votes	location	rest_type	dish_liked	cuisines	approx_cost(for two people)	listed_in(type)	listed_in(city)
0	Jalsa	Yes	Yes	4.1	775	Banashankari	Casual Dining	Pasta, Lunch Buffet, Masala Papad, Paneer Laja...	North Indian, Mughlai, Chinese	800	Buffet	Banashankari
1	Spice Elephant	Yes	No	4.1	787	Banashankari	Casual Dining	Momos, Lunch Buffet, Chocolate Nirvana, Thai G...	Chinese, North Indian, Thai	800	Buffet	Banashankari
2	San Churro Cafe	Yes	No	3.8	918	Banashankari	Cafe, Casual Dining	Churros, Cannelloni, Minestrone Soup, Hot Choc...	Cafe, Mexican, Italian	800	Buffet	Banashankari
3	Addhuri Udupi Bhojana	No	No	3.7	88	Banashankari	Quick Bites	Masala Dosa	South Indian, North Indian	300	Buffet	Banashankari
4	Grand Village	No	No	3.8	166	Basavanagudi	Casual Dining	Panipuri, Gol Gappe	North Indian, Rajasthani	600	Buffet	Banashankari
...	...	...	...	...	...	...	...	...	...	...	...	...
51712	Best Brews - Four Points by Sheraton Bengaluru...	No	No	3.6	27	Whitefield	Bar	0	Continental	1,500	Pubs and bars	Whitefield
51713	Vinod Bar And Restaurant	No	No	0.0	0	Whitefield	Bar	0	Finger Food	600	Pubs and bars	Whitefield
51714	Plunge - Sheraton Grand Bengaluru Whitefield H...	No	No	0.0	0	Whitefield	Bar	0	Finger Food	2,000	Pubs and bars	Whitefield
51715	Chime - Sheraton Grand Bengaluru Whitefield Ho...	No	Yes	4.3	236	ITPL Main Road, Whitefield	Bar	Cocktails, Pizza, Buttermilk	Finger Food	2,500	Pubs and bars	Whitefield
51716	The Nest - The Den Bengaluru	No	No	3.4	13	ITPL Main Road, Whitefield	Bar, Casual Dining	0	Finger Food, North Indian, Continental	1,500	Pubs and bars	Whitefield

51717 rows × 12 columns

df.isnull().sum()

name                           0
online_order                   0
book_table                     0
rate                           0
votes                          0
location                       0
rest_type                      0
dish_liked                     0
cuisines                       0
approx_cost(for two people)    0
listed_in(type)                0
listed_in(city)                0
dtype: int64

=> Votes Column

df['votes'].unique()

array([ 775,  787,  918, ..., 4957, 2382,  843], dtype=int64)

df['votes'].value_counts()

0       10027
4        1140
6         992
7         872
9         738
        ...  
3673        1
1862        1
3909        1
2155        1
843         1
Name: votes, Length: 2328, dtype: int64

Votes contain purely integers, so no need of cleaning it.

=> Location Column

There are two columns by the name location. One is 'location'other is 'listed_in(city)' so I am going to drop the one that has got least unique values

df['location'].unique()

array(['Banashankari', 'Basavanagudi', 'Mysore Road', 'Jayanagar',
       'Kumaraswamy Layout', 'Rajarajeshwari Nagar', 'Vijay Nagar',
       'Uttarahalli', 'JP Nagar', 'South Bangalore', 'City Market',
       'Nagarbhavi', 'Bannerghatta Road', 'BTM', 'Kanakapura Road',
       'Bommanahalli', 0, 'CV Raman Nagar', 'Electronic City', 'HSR',
       'Marathahalli', 'Sarjapur Road', 'Wilson Garden', 'Shanti Nagar',
       'Koramangala 5th Block', 'Koramangala 8th Block', 'Richmond Road',
       'Koramangala 7th Block', 'Jalahalli', 'Koramangala 4th Block',
       'Bellandur', 'Whitefield', 'East Bangalore', 'Old Airport Road',
       'Indiranagar', 'Koramangala 1st Block', 'Frazer Town', 'RT Nagar',
       'MG Road', 'Brigade Road', 'Lavelle Road', 'Church Street',
       'Ulsoor', 'Residency Road', 'Shivajinagar', 'Infantry Road',
       'St. Marks Road', 'Cunningham Road', 'Race Course Road',
       'Commercial Street', 'Vasanth Nagar', 'HBR Layout', 'Domlur',
       'Ejipura', 'Jeevan Bhima Nagar', 'Old Madras Road', 'Malleshwaram',
       'Seshadripuram', 'Kammanahalli', 'Koramangala 6th Block',
       'Majestic', 'Langford Town', 'Central Bangalore', 'Sanjay Nagar',
       'Brookefield', 'ITPL Main Road, Whitefield',
       'Varthur Main Road, Whitefield', 'KR Puram',
       'Koramangala 2nd Block', 'Koramangala 3rd Block', 'Koramangala',
       'Hosur Road', 'Rajajinagar', 'Banaswadi', 'North Bangalore',
       'Nagawara', 'Hennur', 'Kalyan Nagar', 'New BEL Road', 'Jakkur',
       'Rammurthy Nagar', 'Thippasandra', 'Kaggadasapura', 'Hebbal',
       'Kengeri', 'Sankey Road', 'Sadashiv Nagar', 'Basaveshwara Nagar',
       'Yeshwantpur', 'West Bangalore', 'Magadi Road', 'Yelahanka',
       'Sahakara Nagar', 'Peenya'], dtype=object)

df['listed_in(city)'].unique()

array(['Banashankari', 'Bannerghatta Road', 'Basavanagudi', 'Bellandur',
       'Brigade Road', 'Brookefield', 'BTM', 'Church Street',
       'Electronic City', 'Frazer Town', 'HSR', 'Indiranagar',
       'Jayanagar', 'JP Nagar', 'Kalyan Nagar', 'Kammanahalli',
       'Koramangala 4th Block', 'Koramangala 5th Block',
       'Koramangala 6th Block', 'Koramangala 7th Block', 'Lavelle Road',
       'Malleshwaram', 'Marathahalli', 'MG Road', 'New BEL Road',
       'Old Airport Road', 'Rajajinagar', 'Residency Road',
       'Sarjapur Road', 'Whitefield'], dtype=object)

df=df.drop(['listed_in(city)'], axis=1)

location=df['location'].value_counts(ascending=False)
location

BTM                      5124
HSR                      2523
Koramangala 5th Block    2504
JP Nagar                 2235
Whitefield               2144
                         ... 
West Bangalore              6
Yelahanka                   6
Jakkur                      3
Rajarajeshwari Nagar        2
Peenya                      1
Name: location, Length: 94, dtype: int64

I am going to replace the least called locations under others category

loc_500=location[location<500]

def make_other_loc(value):
    if(value in loc_500):
        return 'others'
    else:
        return value
df['location']=df['location'].apply(make_other_loc)
df['location'].value_counts(ascending=False)

others                   8141
BTM                      5124
HSR                      2523
Koramangala 5th Block    2504
JP Nagar                 2235
Whitefield               2144
Indiranagar              2083
Jayanagar                1926
Marathahalli             1846
Bannerghatta Road        1630
Bellandur                1286
Electronic City          1258
Koramangala 1st Block    1238
Brigade Road             1218
Koramangala 7th Block    1181
Koramangala 6th Block    1156
Sarjapur Road            1065
Ulsoor                   1023
Koramangala 4th Block    1017
MG Road                   918
Banashankari              906
Kalyan Nagar              853
Richmond Road             812
Frazer Town               727
Malleshwaram              725
Basavanagudi              684
Residency Road            675
Banaswadi                 664
Brookefield               658
New BEL Road              649
Kammanahalli              648
Rajajinagar               591
Church Street             569
Lavelle Road              529
Shanti Nagar              511
Name: location, dtype: int64

=> Cuisines Column

df['cuisines'].unique()

array(['North Indian, Mughlai, Chinese', 'Chinese, North Indian, Thai',
       'Cafe, Mexican, Italian', ...,
       'North Indian, Street Food, Biryani', 'Chinese, Mughlai',
       'North Indian, Chinese, Arabian, Momos'], dtype=object)

cuisines=df['cuisines'].value_counts(ascending=False)
cuisines

North Indian                                   2913
North Indian, Chinese                          2385
South Indian                                   1828
Biryani                                         918
Bakery, Desserts                                911
                                               ... 
North Indian, Chinese, South Indian, Juices       1
North Indian, Chinese, Kebab, Mughlai             1
Chinese, Vietnamese, Thai, Malaysian              1
Arabian, Lebanese, Chinese, Rolls                 1
North Indian, Chinese, Arabian, Momos             1
Name: cuisines, Length: 2724, dtype: int64

cuisines_100=cuisines[cuisines<100]
cuisines_100

North Indian, Continental, Chinese             97
Juices                                         94
Fast Food, North Indian                        93
Bengali, North Indian                          93
Beverages, Juices                              90
                                               ..
North Indian, Chinese, South Indian, Juices     1
North Indian, Chinese, Kebab, Mughlai           1
Chinese, Vietnamese, Thai, Malaysian            1
Arabian, Lebanese, Chinese, Rolls               1
North Indian, Chinese, Arabian, Momos           1
Name: cuisines, Length: 2655, dtype: int64

def make_other_cuisines(value):
    if(value in cuisines_100):
        return 'others'
    else:
        return value
df['cuisines']=df['cuisines'].apply(make_other_cuisines)
df['cuisines'].value_counts(ascending=False)

others                                 26505
North Indian                            2913
North Indian, Chinese                   2385
South Indian                            1828
Biryani                                  918
                                       ...  
South Indian, Chinese, North Indian      105
Italian, Pizza                           105
North Indian, Mughlai, Chinese           104
South Indian, Fast Food                  104
North Indian, Chinese, Seafood           102
Name: cuisines, Length: 70, dtype: int64

=> approx_cost(for two people) Column

df['approx_cost(for two people)'].unique()

array(['800', '300', '600', '700', '550', '500', '450', '650', '400',
       '900', '200', '750', '150', '850', '100', '1,200', '350', '250',
       '950', '1,000', '1,500', '1,300', '199', '80', '1,100', '160',
       '1,600', '230', '130', '50', '190', '1,700', 0, '1,400', '180',
       '1,350', '2,200', '2,000', '1,800', '1,900', '330', '2,500',
       '2,100', '3,000', '2,800', '3,400', '40', '1,250', '3,500',
       '4,000', '2,400', '2,600', '120', '1,450', '469', '70', '3,200',
       '60', '560', '240', '360', '6,000', '1,050', '2,300', '4,100',
       '5,000', '3,700', '1,650', '2,700', '4,500', '140'], dtype=object)

I am going to remove the comas and make the values an integer

def make_integer(value):
    value=str(value)
    if ',' in value:
        value= value.replace(',','')
        return int(value)
    else:
        return int(value)
df['approx_cost(for two people)']=df['approx_cost(for two people)'].apply(make_integer)
df['approx_cost(for two people)'].unique()

array([ 800,  300,  600,  700,  550,  500,  450,  650,  400,  900,  200,
        750,  150,  850,  100, 1200,  350,  250,  950, 1000, 1500, 1300,
        199,   80, 1100,  160, 1600,  230,  130,   50,  190, 1700,    0,
       1400,  180, 1350, 2200, 2000, 1800, 1900,  330, 2500, 2100, 3000,
       2800, 3400,   40, 1250, 3500, 4000, 2400, 2600,  120, 1450,  469,
         70, 3200,   60,  560,  240,  360, 6000, 1050, 2300, 4100, 5000,
       3700, 1650, 2700, 4500,  140], dtype=int64)

=> Restaurant Type Column

df['rest_type'].unique()

array(['Casual Dining', 'Cafe, Casual Dining', 'Quick Bites',
       'Casual Dining, Cafe', 'Cafe', 'Quick Bites, Cafe',
       'Cafe, Quick Bites', 'Delivery', 'Mess', 'Dessert Parlor',
       'Bakery, Dessert Parlor', 'Pub', 'Bakery', 'Takeaway, Delivery',
       'Fine Dining', 'Beverage Shop', 'Sweet Shop', 'Bar',
       'Beverage Shop, Quick Bites', 'Confectionery',
       'Quick Bites, Beverage Shop', 'Dessert Parlor, Sweet Shop',
       'Bakery, Quick Bites', 'Sweet Shop, Quick Bites', 'Kiosk',
       'Food Truck', 'Quick Bites, Dessert Parlor',
       'Beverage Shop, Dessert Parlor', 'Takeaway', 'Pub, Casual Dining',
       'Casual Dining, Bar', 'Dessert Parlor, Beverage Shop',
       'Quick Bites, Bakery', 'Dessert Parlor, Quick Bites',
       'Microbrewery, Casual Dining', 'Lounge', 'Bar, Casual Dining',
       'Food Court', 'Cafe, Bakery', 0, 'Dhaba',
       'Quick Bites, Sweet Shop', 'Microbrewery',
       'Food Court, Quick Bites', 'Pub, Bar', 'Casual Dining, Pub',
       'Lounge, Bar', 'Food Court, Dessert Parlor',
       'Casual Dining, Sweet Shop', 'Food Court, Casual Dining',
       'Casual Dining, Microbrewery', 'Sweet Shop, Dessert Parlor',
       'Bakery, Beverage Shop', 'Lounge, Casual Dining',
       'Cafe, Food Court', 'Beverage Shop, Cafe', 'Cafe, Dessert Parlor',
       'Dessert Parlor, Cafe', 'Dessert Parlor, Bakery',
       'Microbrewery, Pub', 'Bakery, Food Court', 'Club',
       'Quick Bites, Food Court', 'Bakery, Cafe', 'Bar, Cafe',
       'Pub, Cafe', 'Casual Dining, Irani Cafee', 'Fine Dining, Lounge',
       'Bar, Quick Bites', 'Bakery, Kiosk', 'Pub, Microbrewery',
       'Microbrewery, Lounge', 'Fine Dining, Microbrewery',
       'Fine Dining, Bar', 'Mess, Quick Bites', 'Dessert Parlor, Kiosk',
       'Bhojanalya', 'Casual Dining, Quick Bites', 'Pop Up', 'Cafe, Bar',
       'Casual Dining, Lounge', 'Bakery, Sweet Shop', 'Microbrewery, Bar',
       'Cafe, Lounge', 'Bar, Pub', 'Lounge, Cafe', 'Club, Casual Dining',
       'Quick Bites, Mess', 'Quick Bites, Meat Shop',
       'Quick Bites, Kiosk', 'Lounge, Microbrewery',
       'Food Court, Beverage Shop', 'Dessert Parlor, Food Court',
       'Bar, Lounge'], dtype=object)

df['rest_type'].value_counts()

Quick Bites                   19132
Casual Dining                 10330
Cafe                           3732
Delivery                       2604
Dessert Parlor                 2263
                              ...  
Dessert Parlor, Kiosk             2
Food Court, Beverage Shop         2
Dessert Parlor, Food Court        2
Sweet Shop, Dessert Parlor        1
Quick Bites, Kiosk                1
Name: rest_type, Length: 94, dtype: int64

rest_types=df['rest_type'].value_counts()
type_less_than_2000=rest_types[rest_types<2000]
type_less_than_2000

Casual Dining, Bar            1154
Bakery                        1141
Beverage Shop                  867
Bar                            697
Food Court                     624
                              ... 
Dessert Parlor, Kiosk            2
Food Court, Beverage Shop        2
Dessert Parlor, Food Court       2
Sweet Shop, Dessert Parlor       1
Quick Bites, Kiosk               1
Name: rest_type, Length: 88, dtype: int64

def make_other_type(value):
    if(value in type_less_than_2000):
        return 'others'
    else:
        return value
df['rest_type']=df['rest_type'].apply(make_other_type)
df['rest_type'].value_counts()

Quick Bites           19132
others                11619
Casual Dining         10330
Cafe                   3732
Delivery               2604
Dessert Parlor         2263
Takeaway, Delivery     2037
Name: rest_type, dtype: int64

=> Listed In type column

df['listed_in(type)'].unique()

array(['Buffet', 'Cafes', 'Delivery', 'Desserts', 'Dine-out',
       'Drinks & nightlife', 'Pubs and bars'], dtype=object)

There are not many types in this category. So no need to clean the data

=> Dishes Liked

df['dish_liked'].unique()

array(['Pasta, Lunch Buffet, Masala Papad, Paneer Lajawab, Tomato Shorba, Dum Biryani, Sweet Corn Soup',
       'Momos, Lunch Buffet, Chocolate Nirvana, Thai Green Curry, Paneer Tikka, Dum Biryani, Chicken Biryani',
       'Churros, Cannelloni, Minestrone Soup, Hot Chocolate, Pink Sauce Pasta, Salsa, Veg Supreme Pizza',
       ...,
       'Noodles, Chicken Noodle, Momos, American Chopsuey, Salad, Manchow Soup, Manchurian',
       'Chicken Quesadilla, Naan, Breakfast Buffet, Cheesecake, Cocktails, Lunch Buffet, Biryani',
       'Biryani, Andhra Meal'], dtype=object)

dish_liked=df['dish_liked'].value_counts(ascending=False)
dish_liked

0                                                                                                    28078
Biryani                                                                                                182
Chicken Biryani                                                                                         73
Friendly Staff                                                                                          69
Waffles                                                                                                 68
                                                                                                     ...  
Butter Chicken, Shawarma Roll, Chicken Shawarama, Chicken Grill, Rolls, Al Faham Chicken, Biryani        1
Filter Coffee, Sandwich, Bonda, Vada, Masala Dosa, Salad, Aloo Curry                                     1
Burgers, Fries, Jumbo Royale Burger, Salads, Peri Peri Chicken Salad, Potato Wedges, Rolls               1
Chaat, Pav Bhaji, Raj Kachori, Buttermilk, Ajwaini Paratha, Tawa Pulav, Sev Puri                         1
Paratha, Dal Makhani, Lassi, Naan, Veg Thali, Chole, Kulcha                                              1
Name: dish_liked, Length: 5272, dtype: int64

Here The Outlier is the Null Value. 28078 of the values are null. Rest have liked what of dish they have liked. So I am going to make the null values as not participants

dish_0=dish_liked[dish_liked==28078]
dish_0

0    28078
Name: dish_liked, dtype: int64

def make_other_dish(value):
    if(value in dish_0):
        return 'not rated item'
    else:
        return value
df['dish_liked']=df['dish_liked'].apply(make_other_dish)
df['dish_liked'].value_counts()

not rated item                                                                                       28078
Biryani                                                                                                182
Chicken Biryani                                                                                         73
Friendly Staff                                                                                          69
Waffles                                                                                                 68
                                                                                                     ...  
Butter Chicken, Shawarma Roll, Chicken Shawarama, Chicken Grill, Rolls, Al Faham Chicken, Biryani        1
Filter Coffee, Sandwich, Bonda, Vada, Masala Dosa, Salad, Aloo Curry                                     1
Burgers, Fries, Jumbo Royale Burger, Salads, Peri Peri Chicken Salad, Potato Wedges, Rolls               1
Chaat, Pav Bhaji, Raj Kachori, Buttermilk, Ajwaini Paratha, Tawa Pulav, Sev Puri                         1
Paratha, Dal Makhani, Lassi, Naan, Veg Thali, Chole, Kulcha                                              1
Name: dish_liked, Length: 5272, dtype: int64

Now I am going to cluster the least liked item into others. That is if people have rated the dish less than 50

dish_50=dish_liked[dish_liked<=50]
dish_50

Coffee                                                                                               42
Rooftop Ambience                                                                                     42
Pizza                                                                                                38
Burgers                                                                                              33
Cocktails                                                                                            29
                                                                                                     ..
Butter Chicken, Shawarma Roll, Chicken Shawarama, Chicken Grill, Rolls, Al Faham Chicken, Biryani     1
Filter Coffee, Sandwich, Bonda, Vada, Masala Dosa, Salad, Aloo Curry                                  1
Burgers, Fries, Jumbo Royale Burger, Salads, Peri Peri Chicken Salad, Potato Wedges, Rolls            1
Chaat, Pav Bhaji, Raj Kachori, Buttermilk, Ajwaini Paratha, Tawa Pulav, Sev Puri                      1
Paratha, Dal Makhani, Lassi, Naan, Veg Thali, Chole, Kulcha                                           1
Name: dish_liked, Length: 5265, dtype: int64

def make_other_dish2(value):
    if(value in dish_50):
        return 'other dishes'
    else:
        return value
df['dish_liked']=df['dish_liked'].apply(make_other_dish2)
df['dish_liked'].value_counts()

not rated item     28078
other dishes       23134
Biryani              182
Chicken Biryani       73
Friendly Staff        69
Waffles               68
Paratha               57
Masala Dosa           56
Name: dish_liked, dtype: int64

df

	name	online_order	book_table	rate	votes	location	rest_type	dish_liked	cuisines	approx_cost(for two people)	listed_in(type)
0	Jalsa	Yes	Yes	4.1	775	Banashankari	Casual Dining	other dishes	North Indian, Mughlai, Chinese	800	Buffet
1	Spice Elephant	Yes	No	4.1	787	Banashankari	Casual Dining	other dishes	others	800	Buffet
2	San Churro Cafe	Yes	No	3.8	918	Banashankari	others	other dishes	others	800	Buffet
3	Addhuri Udupi Bhojana	No	No	3.7	88	Banashankari	Quick Bites	Masala Dosa	South Indian, North Indian	300	Buffet
4	Grand Village	No	No	3.8	166	Basavanagudi	Casual Dining	other dishes	others	600	Buffet
...	...	...	...	...	...	...	...	...	...	...	...
51712	Best Brews - Four Points by Sheraton Bengaluru...	No	No	3.6	27	Whitefield	others	not rated item	Continental	1500	Pubs and bars
51713	Vinod Bar And Restaurant	No	No	0.0	0	Whitefield	others	not rated item	Finger Food	600	Pubs and bars
51714	Plunge - Sheraton Grand Bengaluru Whitefield H...	No	No	0.0	0	Whitefield	others	not rated item	Finger Food	2000	Pubs and bars
51715	Chime - Sheraton Grand Bengaluru Whitefield Ho...	No	Yes	4.3	236	others	others	other dishes	Finger Food	2500	Pubs and bars
51716	The Nest - The Den Bengaluru	No	No	3.4	13	others	others	not rated item	others	1500	Pubs and bars

51717 rows × 11 columns

Step 3: Statistical analysis & visualization

After Data Cleaning, The Statistical Values like mean, standard deviation, quartiles can be found from the function describe()

df.describe()

	rate	votes	approx_cost(for two people)
count	51717.000000	51717.000000	51717.000000
mean	2.981209	283.697527	551.715587
std	1.516766	803.838853	439.717709
min	0.000000	0.000000	0.000000
25%	3.000000	7.000000	300.000000
50%	3.600000	41.000000	400.000000
75%	3.900000	198.000000	650.000000
max	4.900000	16832.000000	6000.000000

Data Visualization

Visulazing Location

I am going to check the number of number times the location that has repeated itself.

# Count plot for location
loc_others=['others']
df=df[df['location'].isin(loc_others)==False]
plt.figure(figsize=(20,20))
plt.title('Vizualtion of Location')
sns.countplot(x=df['location'])
plt.xticks(rotation=90);

Fig 1 is showing the number of restaturants area wise in Bengaluru.

Visualizing online order facility

plt.figure(figsize=(6,6))
plt.title('Vizualtion of Online Order facility')
sns.countplot(x=df['online_order'])
plt.show()

Fig 2. Shows the Online Order facility of restaurants as to How many restaurants provide online ordering facility. About 30000+ restaurants in Bengaluru provide online ording facility. About 20000+ restaurants do not provide online ordering facility.

Visualizing table Booking Facility

plt.figure(figsize=(6,6))
fig = px.violin(df, y="book_table", title='Vizualtion of Table booking facility')
fig.show();

<Figure size 432x432 with 0 Axes>

Fig 3. Shows the reservation System. Well about 80% restaurants do not provide reseveration. 20% of them do provide reservation facility.

Visualizing Restuarant Type

fig = px.pie(df, values=df['rest_type'].value_counts(), names=df['rest_type'].unique(), title='Types of the Restaurant')
fig.show()

Fig 4. shows the percentage of type of the restaurant. About 37% of the restaurants are casual dining centers, 20% are quick bites, 7.2% are cafes, 4.4% are Dessert Parlors.

Visualizing dishes liked

Visualizing the dish_liked Column

d=df.groupby(['dish_liked'])['dish_liked'].count()
plt.title('Vizualtion of dishes liked')
d.plot(kind='bar',figsize=(10,8));

Fig 5. Shows the Type of the dish That is been liked by the customers. The rest of the items liked are Chiken Biryani, Massala Dosa, Paratha, waffles repsectively in the decreasing percentages. This data is insufficient to say which the best dish that people have liked as the percentage of the people who have not rated the dish item is more and there are small dish items in the list as well.

Visualizing cuisines

plt.figure(figsize=(16,16))
values=['others']
df= df[df['cuisines'].isin(values) == False]
sns.countplot(x=df['cuisines'])
plt.xticks(rotation=90)
plt.title('Vizualization of Cuisines')
plt.show()

Fig 6. shows the cuisines prepared in the restaunts. Restaurants preparing North Indian dish are popular. Next to that, we have North Indian Chinese, South Indian, Biryani spots, Bakery, Desserts are respectively popular in the decreasing order.

Visualizing the category of restaurant (listed_in (type) column)

fig = px.pie(df, values=df['listed_in(type)'].value_counts(), names=df['listed_in(type)'].unique(), title='Categories of the Restaurant')
fig.show()

Fig 7: Shows the categories of restaurant. 50.2%(25942) restaurants are Buffet's. 34.4%(17779) are cafes. Fast food joints are off 6.95% (3593). Bakery and sweet parlours which come under Desserts are 3.33%(1723). Dine-outs are 2.13%(1101). Drinks and Nightlife restaurants are 1.71%(882). Bar and Pubs constitute 1.35%(697)

Visualizing rating out of 5

Zero_values=[0]
df= df[df['rate'].isin(Zero_values) == False]
plt.title('Visualization of rating')
sns.histplot(data=df, y="rate");

Fig 8: shows the distribution of rating versus the number of ratings. The Average rating about 4.0

visualizing approximate cost for 2 people

sns.displot(data=df, x="approx_cost(for two people)");

Fig 9: shows the distribution of cost for two people dining versus the number of restaurants having the same cost.

Step 4: Analysis of data for the following questions

I want to use this data to solve the following questions

1. How many People have rated restaurants?
2. How is the online ordering facility affect the rating?
3. How does online booking facility affect the rating?
4. What restaurants in what all locations do they provide online ordering facility?
5. What restaurants in what all locations do they provide online booking facility?
6. How much does it cost for 2 people dining based on different location?
7. What type of restaurant should be opened based on different location?
8. What type of cuisines should be prepared for best business?

1. How many People have rated restaurants?

sns.barplot(data=df, x="rate", y="votes")
plt.title('Relationship between Rating and Votes for each rating')
plt.xticks(rotation=90);

Fig 10: shows the distribution of ratings with respect to the number of people who have voted. Regardless of the restaurant, 5000+ people have voted 4.7 rating. The next rating is 4.9 voted by 4000+ people. There are bad ratings too.

2. How is the online ordering facility affect the rating?

plt.figure(figsize=(6,6))
plt.title('Relationship between rating and online ordering facility')
sns.boxplot(x='online_order',y='rate', data=df);

Fig 11: shows the online ordering facility versus rating. The average rating that is given for a restuarant which has online ordering facility is more than the restaurants which do not provide online ordering facility. Surprisingly the restaurants which do not provide online service is more than the ones that provide online servicing.

3. How does online booking facility affect the rating?

plt.figure(figsize=(6,6))
plt.title('Relationship between rating and online booking facility')
sns.boxplot(x='book_table',y='rate', data=df);

Fig 12. shows the online ordering table booking versus rating. The average rating that is given for a restuarant which has online table booking facility is more than the restaurants which do not provide online table booking facility.

4. What restaurants in what all locations do they provide online ordering facility?

Since there are many Location values, I am going to group them first and create a pivot table, later I will be ploting a bar chart

x=df.groupby(['location', 'online_order'])['name'].count()
x.to_csv('online_order_with_location.csv')
x=pd.read_csv('online_order_with_location.csv')
x=pd.pivot_table(x,values=None,index=['location'],columns=['online_order'],fill_value=0,aggfunc=np.sum)
x

	name
online_order	No	Yes
location
BTM	597	1400
Banashankari	195	235
Banaswadi	112	140
Bannerghatta Road	217	411
Basavanagudi	105	227
Bellandur	195	337
Brigade Road	166	219
Brookefield	100	173
Church Street	85	119
Electronic City	189	203
Frazer Town	119	205
HSR	200	728
Indiranagar	234	524
JP Nagar	274	500
Jayanagar	225	560
Kalyan Nagar	102	184
Kammanahalli	85	106
Koramangala 1st Block	93	363
Koramangala 4th Block	122	208
Koramangala 5th Block	177	420
Koramangala 6th Block	134	278
Koramangala 7th Block	134	245
Lavelle Road	55	36
MG Road	149	152
Malleshwaram	153	188
Marathahalli	233	473
New BEL Road	84	136
Rajajinagar	121	146
Residency Road	188	82
Richmond Road	223	71
Sarjapur Road	97	290
Shanti Nagar	97	103
Ulsoor	113	223
Whitefield	336	451

x.plot(kind='bar',figsize=(20,8), title='Visualization of online ordering based on different locaton');

Fig 10. shows the distribution of online ordering restaurants location wise in Bengaluru. Excluding the restaurants which do allow online ordering and not ordering facility in minor areas are showing the highest counts. BTM Layout has more restaurants which are providing online ordering service. In Every location, the restaurants which provide online ordering are more than the offlines ones.

5. What restaurants in what all locations do they provide online booking facility?

y=df.groupby(['location', 'book_table'])['name'].count()
y.to_csv('book_table_with_location.csv')
y=pd.read_csv('book_table_with_location.csv')
y=pd.pivot_table(y,values=None,index=['location'],columns=['book_table'],fill_value=0,aggfunc=np.sum)
y

	name
book_table	No	Yes
location
BTM	1973	24
Banashankari	410	20
Banaswadi	252	0
Bannerghatta Road	610	18
Basavanagudi	326	6
Bellandur	508	24
Brigade Road	364	21
Brookefield	264	9
Church Street	168	36
Electronic City	379	13
Frazer Town	324	0
HSR	882	46
Indiranagar	666	92
JP Nagar	731	43
Jayanagar	739	46
Kalyan Nagar	273	13
Kammanahalli	187	4
Koramangala 1st Block	456	0
Koramangala 4th Block	282	48
Koramangala 5th Block	530	67
Koramangala 6th Block	379	33
Koramangala 7th Block	343	36
Lavelle Road	54	37
MG Road	257	44
Malleshwaram	304	37
Marathahalli	683	23
New BEL Road	207	13
Rajajinagar	260	7
Residency Road	232	38
Richmond Road	279	15
Sarjapur Road	361	26
Shanti Nagar	200	0
Ulsoor	301	35
Whitefield	748	39

y.plot(kind='bar',figsize=(20,8), title='Visualization of online table booking facility based on different locaton');

Fig 11. shows the table booking facility based on location. About 5000 restaurants in BTM Layout do not prvide online table booking facility. This is true even with the other restaurants in the other locations, though the number of restaurants vary

6. How much does it cost for 2 people dining based on different location?

z=df.groupby(['location','approx_cost(for two people)'])['name'].count()
z.to_csv('cost_table_with_location.csv')
z=pd.read_csv('cost_table_with_location.csv')
z=pd.pivot_table(z,values=None,index=['location'],fill_value=0,aggfunc=np.mean)
z

	approx_cost(for two people)	name
location
BTM	573.333333	83.208333
Banashankari	568.421053	22.631579
Banaswadi	479.411765	14.823529
Bannerghatta Road	584.210526	33.052632
Basavanagudi	603.125000	20.750000
Bellandur	673.809524	25.333333
Brigade Road	573.888889	21.388889
Brookefield	460.000000	18.200000
Church Street	650.000000	13.600000
Electronic City	666.666667	18.666667
Frazer Town	468.750000	20.250000
HSR	484.000000	46.400000
Indiranagar	643.333333	31.583333
JP Nagar	607.142857	36.857143
Jayanagar	635.714286	37.380952
Kalyan Nagar	513.888889	15.888889
Kammanahalli	466.666667	12.733333
Koramangala 1st Block	496.875000	28.500000
Koramangala 4th Block	743.333333	22.000000
Koramangala 5th Block	597.368421	31.421053
Koramangala 6th Block	600.000000	24.235294
Koramangala 7th Block	558.333333	21.055556
Lavelle Road	840.000000	9.100000
MG Road	1105.882353	17.705882
Malleshwaram	667.619048	16.238095
Marathahalli	634.782609	30.695652
New BEL Road	666.666667	10.476190
Rajajinagar	527.777778	14.833333
Residency Road	1072.727273	12.272727
Richmond Road	947.222222	16.333333
Sarjapur Road	618.181818	17.590909
Shanti Nagar	395.000000	20.000000
Ulsoor	753.333333	22.400000
Whitefield	935.714286	28.107143

z.plot(kind='bar',figsize=(20,8),title='Visualization of approximate cost for 2 people based on different locaton');

Fig 12: shows the distribution of approximate cost for two people people dining in all the restaurants in the respective area. Lavelle Road restaurants are much costlier than any other areas, also Recidency road, Richmond Road, Ulsoor, church street, Indiranagr are costlier. Other area are comparetively higher.

7. What type of restaurant should be opened based on different location?

p=df.groupby(['location', 'rest_type','listed_in(type)']).count()
p.to_csv('Rest_table_with_location.csv')
p=pd.read_csv('Rest_table_with_location.csv')
p=pd.pivot_table(p,values=None,index=['location'],columns=['rest_type','listed_in(type)'],fill_value=0,aggfunc=np.sum)
p

	approx_cost(for two people)										...	votes
rest_type	Cafe				Casual Dining					Delivery	...	Quick Bites	Takeaway, Delivery		others
listed_in(type)	Cafes	Delivery	Desserts	Dine-out	Buffet	Cafes	Delivery	Desserts	Dine-out	Delivery	...	Dine-out	Delivery	Desserts	Buffet	Cafes	Delivery	Desserts	Dine-out	Drinks & nightlife	Pubs and bars
location
BTM	19	34	0	22	2	0	140	0	82	92	...	469	129	3	5	8	155	41	35	13	9
Banashankari	9	8	1	7	3	0	32	0	34	12	...	117	2	1	0	0	22	20	17	11	0
Banaswadi	12	8	2	10	0	0	17	0	17	0	...	58	10	4	0	0	21	13	8	2	0
Bannerghatta Road	10	10	1	9	0	0	80	0	56	31	...	130	19	5	0	3	36	21	29	1	0
Basavanagudi	3	9	0	3	2	0	18	1	21	0	...	96	3	0	0	0	16	16	3	3	0
Bellandur	11	0	0	11	4	0	43	0	46	18	...	112	10	0	6	1	45	26	48	6	6
Brigade Road	14	9	3	9	0	0	38	0	36	0	...	41	0	0	0	0	29	22	31	19	6
Brookefield	3	0	0	3	0	0	20	0	21	2	...	61	3	0	2	0	30	13	15	0	0
Church Street	11	0	0	10	0	0	25	0	33	0	...	25	0	0	5	0	8	5	5	1	2
Electronic City	3	2	0	2	2	0	34	1	45	8	...	101	11	0	0	1	28	14	20	7	5
Frazer Town	7	14	0	6	0	0	32	0	16	10	...	41	10	3	0	1	24	28	10	0	0
HSR	9	8	1	9	3	0	101	0	50	71	...	127	62	5	0	0	82	28	14	0	3
Indiranagar	23	25	8	17	14	0	99	2	56	58	...	88	18	4	1	5	63	26	9	5	7
JP Nagar	12	3	2	12	17	0	108	0	100	42	...	119	34	2	0	3	57	26	18	6	0
Jayanagar	14	10	6	12	8	0	102	0	59	2	...	153	6	0	0	2	55	30	14	0	0
Kalyan Nagar	9	5	0	8	1	2	37	2	39	15	...	58	2	0	0	0	12	10	4	0	0
Kammanahalli	7	3	2	4	0	0	17	0	18	6	...	44	5	2	0	2	10	2	6	4	0
Koramangala 1st Block	9	13	0	12	0	0	28	0	18	38	...	106	24	0	0	0	33	16	12	3	4
Koramangala 4th Block	18	11	0	18	9	0	26	0	29	44	...	24	10	4	0	0	19	16	15	15	9
Koramangala 5th Block	23	16	0	19	10	0	60	0	68	11	...	60	10	4	0	0	60	25	29	0	3
Koramangala 6th Block	11	10	0	7	9	0	46	0	39	11	...	76	22	4	0	0	18	9	24	8	3
Koramangala 7th Block	17	6	0	11	0	0	19	0	32	55	...	58	5	0	5	0	27	29	15	0	3
Lavelle Road	2	1	0	2	0	0	0	0	0	0	...	0	0	0	5	0	21	14	15	6	6
MG Road	22	12	0	18	10	0	10	0	11	0	...	43	0	0	0	10	32	9	39	11	4
Malleshwaram	15	3	2	9	7	0	15	0	22	0	...	73	2	2	2	0	37	30	23	2	2
Marathahalli	5	4	1	3	3	0	77	1	64	34	...	141	28	2	6	0	57	27	25	6	2
New BEL Road	3	7	0	3	2	0	22	0	19	7	...	46	4	0	0	1	14	6	8	2	2
Rajajinagar	3	0	2	0	3	0	16	0	25	3	...	87	7	2	0	0	7	10	4	1	1
Residency Road	6	5	0	6	5	0	15	0	28	3	...	31	2	0	0	0	0	0	48	30	18
Richmond Road	10	1	0	10	9	0	30	0	45	5	...	53	5	0	5	0	10	20	30	6	6
Sarjapur Road	2	5	5	1	5	0	32	0	32	39	...	64	12	3	0	0	34	21	8	1	1
Shanti Nagar	9	0	0	5	0	0	30	3	18	4	...	54	0	0	0	0	15	11	5	0	0
Ulsoor	0	0	0	0	0	0	15	0	11	7	...	84	0	0	0	0	29	13	15	8	4
Whitefield	12	5	3	10	6	0	79	0	87	49	...	136	25	2	1	0	72	29	57	16	5

34 rows × 248 columns

p.plot(kind='bar',figsize=(20,8),title='Visualization of type of restaurant based on different locaton and cost');

Fig 13: shows the type of the restaurant and their categories. The Delivery Type of restaraunts are more in number than a buffet, pubs and bars.

8. What type of cuisines should be prepared for best business?

q=df.groupby(['location', 'cuisines']).count()
q.to_csv('cuisines_table_with_location.csv')
q=pd.read_csv('cuisines_table_with_location.csv')
q=pd.pivot_table(q,values=None,index=['location'],columns=['cuisines'],fill_value=0,aggfunc=np.sum)
q

	approx_cost(for two people)										...	votes
cuisines	Andhra	Andhra, Biryani	Arabian	Bakery	Bakery, Desserts	Bakery, Fast Food	Beverages	Beverages, Desserts	Beverages, Fast Food	Biryani	...	Pizza, Fast Food	South Indian	South Indian, Biryani	South Indian, Chinese	South Indian, Chinese, North Indian	South Indian, Fast Food	South Indian, North Indian	South Indian, North Indian, Chinese	South Indian, North Indian, Chinese, Street Food	Street Food
location
BTM	10	16	31	30	24	0	60	2	32	77	...	3	59	12	16	10	0	11	67	9	22
Banashankari	2	0	2	5	21	0	0	0	0	12	...	5	55	4	2	6	5	14	18	16	5
Banaswadi	0	0	2	4	8	1	2	0	0	15	...	1	27	7	2	0	0	8	8	3	5
Bannerghatta Road	7	11	2	14	15	2	2	0	4	9	...	3	20	3	2	0	0	21	27	3	17
Basavanagudi	0	0	0	13	9	0	0	8	6	6	...	2	84	3	3	0	5	0	29	3	15
Bellandur	6	2	4	6	20	1	0	4	13	20	...	4	20	4	4	0	0	4	13	8	11
Brigade Road	0	0	0	22	0	4	0	0	7	0	...	3	12	3	20	0	0	13	22	0	0
Brookefield	0	0	2	8	5	0	7	6	12	10	...	5	24	5	0	0	0	4	18	0	7
Church Street	11	0	0	0	0	0	0	0	7	0	...	1	5	0	0	0	0	0	0	8	0
Electronic City	7	10	2	6	13	6	1	3	3	5	...	5	21	0	2	0	2	8	21	2	4
Frazer Town	0	0	0	16	23	10	0	0	4	9	...	4	37	7	7	0	6	0	8	0	6
HSR	10	2	8	10	33	5	11	0	8	27	...	5	56	9	0	0	12	10	1	25	12
Indiranagar	14	2	0	17	18	0	3	0	6	33	...	4	41	6	1	1	7	3	18	2	8
JP Nagar	4	8	2	10	50	2	0	0	10	11	...	2	46	9	2	1	0	7	42	16	7
Jayanagar	8	4	2	6	11	11	13	13	11	19	...	2	86	4	10	4	7	22	49	14	9
Kalyan Nagar	4	0	4	4	18	3	2	0	6	14	...	6	11	0	0	0	0	2	4	0	5
Kammanahalli	0	0	13	0	4	0	0	0	0	6	...	0	16	5	0	0	0	0	0	5	2
Koramangala 1st Block	0	0	0	12	29	1	0	0	7	15	...	0	3	0	6	0	0	9	0	0	0
Koramangala 4th Block	0	0	0	0	22	0	3	0	0	0	...	3	4	0	0	0	0	13	0	5	0
Koramangala 5th Block	15	0	6	0	28	1	20	20	6	13	...	1	15	0	0	0	0	0	15	9	3
Koramangala 6th Block	0	9	0	5	13	1	0	0	0	13	...	9	41	0	0	0	0	4	9	0	0
Koramangala 7th Block	0	0	0	10	11	11	5	0	0	26	...	2	11	10	5	0	0	0	10	0	5
Lavelle Road	0	0	0	20	0	0	0	0	6	0	...	0	0	0	0	0	0	0	0	0	0
MG Road	0	0	0	2	1	0	0	0	0	0	...	11	15	0	0	0	0	0	0	2	3
Malleshwaram	0	4	0	9	8	2	2	2	0	2	...	9	54	4	0	0	7	3	12	1	5
Marathahalli	10	5	0	12	24	4	6	6	6	7	...	4	28	4	9	0	0	5	11	0	10
New BEL Road	0	0	2	3	6	2	0	0	2	5	...	4	12	2	0	1	0	0	6	0	4
Rajajinagar	0	0	0	6	10	1	0	0	0	12	...	2	41	13	8	2	2	2	7	8	8
Residency Road	0	10	0	10	5	0	15	0	2	14	...	0	0	10	10	0	0	0	0	0	4
Richmond Road	0	0	0	8	0	22	0	0	0	5	...	0	19	0	0	0	0	7	6	0	0
Sarjapur Road	4	3	2	13	30	4	0	0	3	16	...	7	18	4	2	0	0	0	15	1	2
Shanti Nagar	0	0	0	10	13	0	0	4	12	12	...	6	29	0	8	0	0	0	5	0	0
Ulsoor	0	0	17	5	27	6	0	0	10	9	...	0	19	0	11	0	0	0	0	0	11
Whitefield	4	6	4	10	18	6	3	1	3	29	...	2	35	1	1	5	1	3	18	4	5

34 rows × 621 columns

q.plot(kind='hist',figsize=(20,8),title='Visualization of restaurant categories based on different locaton');

Fig 14: Shows the variation different cuisines

Step5: Summary and Conclusion

Summary

The above analysis can be summarized as follows

• The dataset was collected from https://www.kaggle.com as a downloadable file and was loaded into Pandas DataFrame
• The dataset contained 51717 rows and 17 columns.
• The uncleaned data contained the following statistical data

            votes
  

  • count 51717.000000
  • mean 283.697527
  • std 803.838853
  • min 0.000000
  • 25% 7.000000
  • 50% 41.000000
  • 75% 198.000000
  • max 16832.000000
• The URL column, address, Menu Column, Phone Number, reviews list columns were not requrired. So I have droped those columns. The other coulmuns null values are replaced with appropriate values. Also the columns are cleaned for outliers or unknown values in them

• The cleaned data contained the following statistical data

    rate                votes       approx_cost(for two people)
  

  count 51717.000000 51717.000000 51717.000000 mean 2.981209 283.697527 551.715587 std 1.516766 803.838853 439.717709 min 0.000000 0.000000 0.000000 25% 3.000000 7.000000 300.000000 50% 3.600000 41.000000 400.000000 75% 3.900000 198.000000 650.000000 max 4.900000 16832.000000 6000.000000

• Fig 2. Shows the Online Order facility of restaurants as to How many restaurants provide online ordering facility. About 30000+ restaurants in Bengaluru provide online ording facility. About 20000+ restaurants do not provide online ordering facility.

• Fig 3. Shows the reservation System. Well about 80% restaurants do not provide reseveration. 20% of them do provide reservation facility.
• Fig 4. shows the percentage of type of the restaurant. About 37% of the restaurants are casual dining centers, 20% are quick bites, 7.2% are cafes, 4.4% are Dessert Parlors.
• Fig 5. Shows the Type of the dish That is been liked by the customers. The rest of the items liked are Chiken Biryani, Massala Dosa, Paratha, waffles repsectively in the decreasing percentages. This data is insufficient to say which the best dish that people have liked as the percentage of the people who have not rated the dish item is more and there are small dish items in the list as well.
• Fig 6. shows the cuisines prepared in the restaunts. Restaurants preparing North Indian dish are popular. Next to that, we have North Indian Chinese, South Indian, Biryani spots, Bakery, Desserts are respectively popular in the decreasing order.
• Fig 7: Shows the categories of restaurant. 50.2%(25942) restaurants are Buffet's. 34.4%(17779) are cafes. Fast food joints are off 6.95% (3593). Bakery and sweet parlours which come under Desserts are 3.33%(1723). Dine-outs are 2.13%(1101). Drinks and Nightlife restaurants are 1.71%(882). Bar and Pubs constitute 1.35%(697)
• Fig 8: shows the distribution of rating versus the number of ratings. The Average rating about 4.0
• Fig 9: shows the distribution of cost for two people dining versus the number of restaurants having the same cost.
• Fig 10. shows the distribution of online ordering restaurants location wise in Bengaluru. Excluding the restaurants which do allow online ordering and not ordering facility in minor areas are showing the highest counts. BTM Layout has more restaurants which are providing online ordering service. In Every location, the restaurants which provide online ordering are more than the offlines ones.
• Fig 11. shows the table booking facility based on location. About 5000 restaurants in BTM Layout do not prvide online table booking facility. This is true even with the other restaurants in the other locations, though the number of restaurants vary
• Fig 12: shows the distribution of approximate cost for two people people dining in all the restaurants in the respective area. Lavelle Road restaurants are much costlier than any other areas, also Recidency road, Richmond Road, Ulsoor, church street, Indiranagr are costlier. Other area are comparetively higher.
• Fig 13: shows the type of the restaurant and their categories. The Delivery Type of restaraunts are more in number than a buffet, pubs and bars.
• Fig 14: Shows the distribution of different cuisines based on votes and approximate cost for two people. The Tabe above that shows that at different locations,

Conclusion

The dataset collected from Zomato can be classified as an NLP problem. The text in the dataset can be further used for sentiment analysis, recommendation system. Based on the above analysis, Biryani, North Indian, South Indian cuisines are most famous BTM Layout is one of the hotspot for dining. Online ordering facility has helped the restaurants to get a higher rating than those do not provide online servicing. As well as restaurants giving Reservation facility via online and offiline have gained more average rating than those do not provide the reservation facility. Fast Food chains and Delivery chains are most popular and have gained higher rating as well.

All in all a if at all one is going to open a restaurant in Bengaluru, online ordering, reservation facilities are important to have a higher rating and to maintain good customers, busy area like, BTM Layout, Whitefield, Indiranagar are going to be pretty hard to servive as the competition is more.

References

www.kaggle.com